In recent time, most of the organizations use various systems and methods to manage their day to day working. Examples of such software tools may include enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and supply chain management (SCM) systems. Each of these software tools is associated with various data repositories and has its own respective data model. Examples of data stored in the data repositories include customer data, transaction data, business research data, and so on.
It is well known, by those skilled in the art, that each of the software tools may use, retrieve and store the data in different formats. Further, in an organization, the various software tools, configured to manage the day to day working of the organization, are often interlinked. This makes it important that the data used and stored by each of these software tools are accurate, updated and consistent.
However, with time, various changes take place in the organization which may involve installation of new applications, re-platforming of applications, migration of data repositories from one vendor to a different vendor and so on. In many situations, this involves migrating the data from an existing data repository to a new data repository.
During this migration, many errors, such as system field limitations, mergers and migrations, data repository migrations, inconsistent standards, discrepancies in data format, difference in structure of data repositories, missing data, data fields filled with default values or nulls, spelling errors and data anomalies, may creep in and degrade the quality of data. This adversely affects the functioning of the new application or re-platformed application or the new data repository. Erroneous data transformation may adversely impact the functioning of the organization. For example the organization having erroneous data transformation processes may suffer from losses arising from extra costs to prepare reconciliations, delay or scrapping migration to a new application or new data repository, failure to bill or collect receivables, inability to deliver orders, failure to meet contracts and so on. The erroneous data can also lead to incompetent future decisions of enterprise and has the potential to negatively impact the growth of the enterprise.